Indoor Temperature Distribution Research Articles

Performance analysis of ground source heat pump systems for constant temperature and humidity air conditioning in industrial buildings: a case study

Abstract The ground source heat pump (GSHP) system has gained widespread popularity for its provision of efficient and environmentally sustainable cooling and heating solutions for buildings. While the performance of GSHP systems has raised significant concerns, research predominantly focuses on residential and office buildings, leaving a gap in understanding their applicability in industrial settings with constant temperature and humidity air conditioning needs. This study aims to address this gap by evaluating the performance of a hybrid ground source heat pump (HGSHP) system implemented within a cigarette factory located in the hot summer and cold winter (HSCW) of China. Through a comprehensive analysis of operational data and long-term monitoring under three cooling conditions, essential parameters including water temperature characteristics, indoor temperature and humidity, system efficiency, power consumption, and soil temperature distribution were examined. The findings revealed that the weighted average coefficient of performance (COP) of the heat pumps (COPhp) and the whole system (COPsys) was decreased from 5.05 to 4.32 and 3.32 to 2.89, respectively. A positive correlation was observed between low cooling load high energy consumption, and low COP. The inlet temperature of the condenser exhibited a declining trend, which was attributed to the intermittent operation of condensation heat recovery. Furthermore, a comprehensive analysis of indoor temperature and humidity distribution within the controlled room was presented and found that almost all the rooms serviced by this system meet the specified design requirements. This research contributes to an enhanced understanding of the viability of HGSHP systems in industrial settings and provides valuable references for improving their energy efficiency and overall performance.

Coupled system for underground heating exchange and solar heat-humidity regulation in greenhouse: Experimental study and simulation analysis

In winter, greenhouses located in mid to high latitudes are required a function of dehumidification and warming due to the low outdoor temperature. Focusing on small spires greenhouses, this study regulates the low temperature and high humidity environment inside the greenhouse and proposes a new-type of greenhouse heat and humidity regulation system by combining composite solid desiccant dehumidification with ground heat exchange system. The greenhouse internal environment is warmed and dehumidified at night by utilizing the adsorption effect of the compound dehumidifier and the sensible heat exchange between the heat exchange system in the ground and the greenhouse. Solar thermal energy is collected during the day using multi-curved tank collector (MTC) for thermal storage in the geothermal system and desiccant desorption. The rationality of pipeline layout is verified through CFD simulation, and the indoor temperature and humidity distribution during the system operation is explored. The experimental results show that the moisture adsorption capacity of the composite desiccant SG-CaCl2 for 10h is 0.511 g/g. The optimal regeneration temperature is 80–100 °C. This system can reduce the nighttime relative humidity of the greenhouse in winter from 96.5 % to 83.6 %, and the average indoor air temperature after warming is 12.8 °C. The heat exchange efficiency of the underground heating exchange system is 0.38. During the desorption phase on daytime, the maximum outlet temperature of MTC is 103.8 °C, and the average heat collection efficiency is 0.42. The system can effectively regulate the air environment in the greenhouse to the appropriate zone for crop growth by combining solar thermal collector technology with recyclable solid dehumidification technology.

Investigation of an FPGA-Based Acoustic Gas Thermometer for Monitoring Indoor Temperature Distribution Two-Dimensionally

Investigation of an FPGA-Based Acoustic Gas Thermometer for Monitoring Indoor Temperature Distribution Two-Dimensionally

Comparative study on numerical simulation of temperature field of farm house with different roof forms

In recent years, the issue of energy consumption in farm buildings has received much attention. The roofs of farm buildings in Northwest China have a variety of roof forms. This paper presents the implementation of first fully confirmed the indoor thermal environment of different roof construction was significantly effected by periodic thermogenesis. In order to determine the indoor temperature distribution of the farmhouse in summer in Ningxia Hui Autonomous Region, we provided the heat transfer coefficient data of the farmhouse envelope, also detailed in the manuscript. Model of Thermal Mass Transport enables fast and accurately simulates the indoor temperature distribution of farmhouses with different roof forms on the same day, taking into account the climate zone of the region. This is despite the phase delay time of indoor temperatures for different roof forms caused by periodic initial temperature boundaries ranged from 1.55 to 2.78h\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{h}$$\\end{document}, and the phase delay angle ranged from 23.25∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} to 41.7∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}. Extensive simulated results revealed individual variability in the role of roof form, demonstrating indoor temperatures in farmhouses corresponding to different climatic zones. In addition, by analyzing and discussing the indoor temperature phase delay angle and delay time for each type of roof forms, statistical results identified the advantages of Non-equal-sloped roof as a local farmhouse roof.

Simplified Simulation Method of Diffusers for Indoor Non-Uniform Temperature Distribution: A Case Study in Shanghai

The specific air jet of a diffuser is formed by the complex internal structure, which affects the outlet airflow distribution of the diffuser directly and the indoor environment distribution indirectly. If the diffusers are developed based on their actual geometry structure and their boundary conditions are set as their inlet flowrate, the simulated indoor temperature distribution will be more accurate. However, it is noted that many problems may arise, such as model complexity, many grid cells, and slow convergence of calculations. Therefore, this paper focuses on a simplified method for four-way square diffusers in a computational fluid dynamics (CFD) simulation of indoor non-uniform temperature distribution. Firstly, the airflow distribution is simulated on the outlet air supply cross-section of the diffuser. Then, according to the outflow characteristics of the diffuser, the diffuser model is simplified and simulated in an experimental room. Finally, the temperature distribution at the 1.2 m height plane is obtained from CFD simulation and compared with the experimental results. The results show that the 68-point air supply opening model can well simulate the effects of the outlet airflow distribution of the diffuser, and the simulated indoor temperature distribution meets the experiment results well. The deviations for three scenarios are between −7.4~1.7% and the average deviation is −3.0%, while the root mean square error of temperature for three scenarios is 0.7 °C, 0.7 °C, and 1.0 °C, respectively. The results also demonstrate the mutual influence of the airflow from different diffusers and the indoor non-uniform temperature distribution under the action of multiple diffusers. The proposed method can contribute to balancing the model complexity and the accuracy in CFD simulation, especially for multiple diffusers in the room.

Data-driven prediction of indoor airflow distribution in naturally ventilated residential buildings using combined CFD simulation and machine learning (ML) approach

Predicting indoor airflow distribution in multi-storey residential buildings is essential for designing energy-efficient natural ventilation systems. The indoor environment significantly impacts human health and well-being, considering the substantial time spent indoors and the potential health and safety risks faced daily. To ensure occupants’ thermal comfort and indoor air quality, airflow simulations in the built environment must be efficient and precise. This study proposes a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we investigate the viability of employing a Deep Neural Network (DNN) model for accurately forecasting indoor airflow dispersion. The quantitative results reveal the DNN’s ability to faithfully reproduce indoor airflow patterns and temperature distributions. Furthermore, DNN approaches to investigate indoor airflow in the residential building achieved an 80% reduction in the time required to anticipate testing scenarios compared with CFD simulation, underscoring the potential for efficient indoor airflow prediction. This research underscores the feasibility and effectiveness of a data-driven approach, enabling swift and accurate indoor airflow predictions in naturally ventilated residential buildings. Such predictive models hold significant promise for optimizing indoor air quality, thermal comfort, and energy efficiency, thereby contributing to sustainable building design and operation.

Computational Fluid Dynamics for Cavity Natural Heat Convection: Numerical Analysis and Optimization in Greenhouse Application

Natural convection in cavity plays a significant role in energy-related field, including the indoor heat transfer analysis in greenhouse with integrated PV roof. In this study, mathematical model is established for two-dimensional heat transfer analysis in greenhouse air cavity, with numerical simulation through computational fluid dynamics (CFD). Main natural convection impact factors, such as system configuration parameters (tilting angle and PV panel unit number) and fluid thermal–physical properties, are investigated with indoor temperature distribution and streamline comparison by finite-volume method (FVD). Preliminary results show that with rising Rayleigh number (Ra), natural convection is enhanced with growing Nusselt number (Nu). Moreover, panel slope tilting angle (θ) highly determines inside heat transfer subregions in terms of the vertical temperature gradient declines with rising θ, improving the temperature distribution uniformity inside. The solar greenhouse example illustrates that with the increasing numbers of panel group numbers (n), the air temperature gradient differences decrease, improving the temperature distribution uniformity inside, which is preferable to built environment accurate control for greenhouse in the practical engineering. This work can provide modeling method support and reference for natural heat convection applications.

Numerical modeling of non-uniform indoor temperature distribution for coordinated air flow control

The indoor thermal environment is usually non-uniformly distributed in parameters such as temperature and velocity. For precise control of indoor environment, it is necessary to figure out the indoor non-uniform temperature distribution (INUTD) that empowers independently zone control according to the different requirements of occupants. Computational Fluid Dynamics (CFD) tool is usually used to obtain the INUTD, but it consumes huge computational resources and time, which may not be suitable for coupling with control system. To address this research question, this study proposes a numerical modeling method for predicting the INUTD and air flowrate response of multiple diffusers for coordinated air flow control. Firstly, we establish a dataset with 5000 cases, along with the results of INUTD for each case. Then two models, namely room thermal response model (RTRModel) and air flowrate prediction model (AFPModel), are developed by machine learning algorithms to predict indoor temperature and supply air flowrate, respectively. The results show that proposed models are both fast in prediction, less than 1 s for each case. The standard deviation of error in RTRModel developed with support vector machine algorithm is 0.0041 while that in AFPModel developed with Convolutional Neural Network algorithm is 0.0198. Further, a comparative analysis has been conducted between the AFPModel with and without optimization algorithm. The results reveal that using optimization algorithm is more accurate, but it takes more time. While numerical modeling can instantaneous response with qualified accuracy. The proposed method can contribute to independently zonal environment control and occupant-centered micro-environment control.

Analysis of factors influencing indoor thermal environment in passive houses in Plateau regions based on regression model

How to make full use of geographical advantage to improve the indoor thermal environment of passive buildings in plateau area has become a hot topic, which is of great significance for building energy conservation and thermal engineering design of passive buildings. This study took a residential house in Lhasa, Tibetas as the research object, obtained indoor air temperature distribution through field measurement. A multiple regression model was used to explore the influence of outdoor meteorological factors on indoor thermal environment. Results showed that the temperature near the south window was the highest mostly higher than 16 °C, and the vertical temperature gradient was higher than 7.5 °C with the solar radiation and outdoor temperature gradually raised during daytime. Besides, the vertical temperature difference is less than 3 °C as the distance from the south window increases, and the air temperature gradually goes down. The standardized regression coefficient (Beta) illustrated that solar radiation has the greatest influence on the indoor thermal environment compared with the outdoor temperature. Therefore, for direct gain passive buildings in Tibetan areas, the potential of reasonably setting transparent heat generation structures to improve the indoor thermal environment is far greater than that of improving the thermal insulation of non-transparent enclosures.

Investigation on indoor airflow and contaminant dispersion of diffuse ceiling ventilation in heating and cooling modes

This paper systematically investigates the indoor airflow and contaminant dispersion of diffuse ceiling ventilation (DCV) for space heating and cooling. A numerical model of the office with DCV is built and validated. The indoor thermal comfort and air quality (IAQ) under different cases are analyzed. Simultaneously, different indices are employed to determine the influence of exhaust positions and air change rates on the system. The results indicate that DCV can provide a satisfying thermal environment both in heating and cooling modes, and there is no reverse flow from the room to the plenum. Besides, the visualizations of indoor airflow, temperature distributions, and mean age of air (MAA) show the different characteristics during heating and cooling. The ranges of contaminant removal effectiveness (CRE) and temperature effectiveness (TE) are 0.87–1.00 and 0.79–1.55 for heating, respectively. For cooling, they are 0.96–1.23 (CRE) and 0.92–1.06 (TE), respectively. Although the TE for heating is slightly higher than that of cooling, the CO2 concentrations and CRE manifest that DCV performs better for space cooling. Moreover, the results show that the exhaust positions influence TE more than CRE, especially for heating. Within the measured air change rates, it is found that the increase of air change per hour (ACH) can aid to the ventilation effectiveness of DCV. Meanwhile, the linear regression equations between ACHs and MAA are obtained. This research is helpful to reveal the flow mechanisms of DCV and guides the design and operation of the system.

Dynamic process simulation of indoor temperature distribution in radiant-convective heating terminals

Intermittent heating can achieve energy savings from a temporal perspective and effectively control building carbon emissions in certain regions. Unlike continuous heating, intermittent heating requires heating terminals with low thermal inertia and a fast room heating speed. Simultaneously, ensuring thermal comfort during the heating process is equally important. Based on this, the newly proposed radiant-convective heating terminal has demonstrated certain potential applicability and thus has gained significant attention. However, current research on radiant-convective heating terminals primarily focuses on steady-state studies of experimental terminal heating performance, lacking dynamic studies on indoor environmental fields. Nevertheless, obtaining a complete, accurate, and continuous indoor heating process solely through experiments is challenging. Therefore, this work simulates the heating process of a radiant-convective terminal through the co-simulation approach of Energy Simulation (ES) and Computational Fluid Dynamics (CFD). ES and CFD are complementary, and therefore coupling them can improve accuracy. Specifically, we establish a co-simulation model for radiant-convective heating terminals and conduct simulations of dynamic convection and radiation modes. The accuracy of the co-simulation results is verified through experimental cases, demonstrating that the convection mode has advantages regarding room temperature rise speed and the heating personnel zone. Furthermore, the radiation mode has advantages in thermal comfort during the stable period. Hence, utilizing a radiant-convective heating terminal with a convection-to-radiation sequence is an effective heating strategy that balances energy savings and thermal comfort under intermittent heating requirements.

Analysis of factors affecting the performance of a novel micro-channel heat pipe PV-Trombe wall system for space heating

Building energy consumption accounts for about 1/3 of the world's total energy consumption. Photovoltaic Trombe wall (PV-TW) is an effective way to reduce a building's energy consumption, but it still has disadvantages such as low energy efficiency and uneven indoor temperature distribution. To address these issues, a novel system with a PV-TW and a micro-channel heat pipe (MCHP) was initially presented in this research. By cooling the PV cell with both air and refrigerant, the system can reduce its surface temperature and improve its electrical performance. To promote thermal performance and user comfort, an air-cooled condenser is also employed to regulate the room's heat transfer. This paper establishes the system's dynamic mathematical model for winter and theoretically evaluates its performance. Besides, the key factors influencing the system’s performance were examined. In winter, the total power generation and average daily electrical efficiency are 3.12 MJ and 13.52%, respectively, with a maximum indoor temperature of 21.46 °C and a daily average thermal efficiency of 56.76%. The system’s performance is all improved with the incremental change of the factors besides the size of the micro-channel flat tube (MAFT), and the performance is not sensitive to the change of the air channel width. The optimal number and width of the MAFT in the evaporator section are 11 and 45 mm. The indoor air-cooled condenser should have about 10 rows of tubes and an ideal air volume of 540 m3/h. The recommended airflow channel thickness is between 5 cm and 10 cm.

Study on choosing mobile sensor location to improve the prediction accuracy of indoor temperature distribution

Nonuniform indoor temperature distributions have been widely introduced due to the increasing concerns brought about by the trade-off between building energy consumption and thermal comfort. Related to this, obtaining real-time indoor temperature field is crucial for control and regulation. Previously, a rapid temperature prediction algorithm based on the contribution ratio of indoor climate (CRI) and sensor information was proposed. Compared with fixed sensors, mobile sensors have better moving flexibilities and higher prediction accuracies. Given that considerable data can be obtained from one sensor, the chosen mobile sensor location has a strong correlation with the prediction accuracy. However, only a few studies have investigated the impact of the sensor location. Thus, this study aimed to determine the optimal mobile sensor location for improving prediction accuracy using the proposed algorithm. Two differently scaled rooms were numerically built, and 1.2 m high points were set as the selected locations. After experimental validation of the simulation results, three factors (moving path, supply/return air, and air velocity) were investigated to determine their effects on prediction accuracy. The results showed that the air velocity was the only significantly impactful factor. Furthermore, two principles of choosing location were proposed: (1) when the percentage of dominant velocity is high, all points should have one identical dominant direction, and (2) when the percentage of dominant velocity is low, points should have a similar actual direction. These principles were verified in both models with a prediction accuracy greater than 85%.

Research on the indoor temperature regulation characteristics of a nearly zero energy building with the nonlinear heat capacity building components: A simple model

With establishment of global carbon reduction targets, energy conservation and emission reduction of building is imperative. By passive building design method, nearly zero energy buildings (nZEB) can decrease building energy consumption requirements. To reduce indoor temperature fluctuation of the nZEB in heating season, the conventional energy storage materials (CESM) or phase change materials (PCM) are utilized in the building components for thermal storage. The heat capacity of the PCM was simplified as ideal nonlinear heat capacity. To reveal the indoor temperature distribution characteristic in the nZEB affected by the building components with or without the nonlinear heat capacity, a simplified mathematical model was established. Several evaluation indicators were put forward to describe indoor temperature regulation difference between the CESM and the PCM. The results show that indoor temperature distribution characteristics of the nZEB with PCM was significantly different with that of the CESM building. The maximum indoor temperature distribution frequency of the PCM building is much larger than that of the CESM building. The maximum indoor temperature distribution frequency of PCM building was more concentrated and occurred close to the phase transition temperature. The maximum indoor temperature distribution frequency of the CESM is not exceed 10%, however, that of the PCM building can reach 42% in the same work condition. The maximum indoor temperature distribution frequency, the indoor comfort guarantee ratio and the latent heat utilization time ratio of the PCM building can be obviously increased by selecting the phase transition temperature close to the average indoor temperature in the heating season. The study work represent that the PCM building components with the nonlinear heat capacity can regulate the indoor temperature distribution frequency by change phase transition temperature.

Development of non-isothermal wall jet integrated zonal model with enhanced accuracy using a multi-flow coefficient calibration method

A zonal model is an intermediate model between a nodal model and a computational fluid dynamics (CFD) model. This type of models has been widely used to predict indoor temperature distribution and airflow patterns by compromising prediction accuracy and computational efficiency. However, the zonal model was mainly used to predict the indoor environment of rooms with natural convection or isothermal air supply in previous literature, neglecting its application in non-isothermal air supply. Since driving flow under non-isothermal air supply is more common in air-conditioned spaces, this paper proposes a jet-integrated zonal model (JIZM) to predict the indoor environment of rooms with non-isothermal wall jet supply, extending the applicability of the zonal modelling technique. Meanwhile, a multi-flow coefficient calibration method was developed to improve the performance of the JIZM using a multi-objective genetic algorithm (MOGA). The applicability of the JIZM was evaluated under typical air supply scenarios. The results demonstrated that the JIZM with the proposed calibration method is able to using limited measurement data with calibrated model parameters to predict the air distributions with reasonable accuracy in a fast way, thus offering a better technique in predicting the vertical temperature distribution in air-conditioned rooms compared with a conventional zonal model.

Experimental research on the influence of a novel passive cooling system on the data center temperature distribution

As artificial intelligence has been leaping forward, data centers (DCs) are only capable of meeting to the daily needs of people through uninterrupted operations. A power outage or cooling system failure can result in high temperatures, trigger data center shutdowns, and even damage IT equipment. In terms of several special DCs, a prolonged cooling system interruption may lead to catastrophic and incalculable consequences. Accordingly, a novel passive cooling system was proposed in this study to address the long-term interruption of air conditioning systems. Liquid nitrogen (LN2) was driven by the pressure of the cryogenic liquid container and flowed into finned tube heat exchangers (FTHEs) in the DC. LN2, i.e., a refrigerant, can absorb heat through vaporization and give FTHEs ultra-low temperature surfaces to drop the indoor temperature. The effects of refrigerant flow rate, temperature, and extreme indoor initial temperature on the indoor temperature distribution were examined. As indicated by the results, increasing the flow rate and decreasing the refrigerant temperature increased the room cooling rate while exacerbating the degree of temperature stratification (TSD). Compared with 35 kg/h, the cooling effect was enhanced by 1.5 times for a flow rate of 50 kg/h, whereas the TSD value near FTHEs was nearly 14 °C/m, approximately twice as high as that of 35 kg/h. Slightly raising the initial ambient temperature led to the rise of the cooling effect, whereas this method slightly affected the temperature stratification. This study provides a novel idea for solving long-term DC cooling system failure while laying a basis for the effect of ultra-low temperature surfaces on ambient temperature distribution.

Remote sensing of indoor thermal environment from outside the building through window opening gap by using infrared camera.

Investigation of housing indoor temperature is important for understanding the comfort, health and living conditions of the local residents. The traditional method to measure indoor temperature is to place sensors at the target places, which is not only expensive but also inconvenient for indoor temperature investigation, especially for the investigation at community and city scale. In this study, a novel method was proposed to obtain the indoor temperatures remotely from outside the building through window opening area using and infrared camera. Compared with the traditional contact measurement method, the proposed remote sensing method could detect the indoor temperature without entering the room. Moreover, the infrared image could reflect the spatial distribution information of indoor temperature. To verify the feasibility and accuracy of this method, an experiment was conducted in a test room under heating, transitional, and cooling conditions with various window opening grades. It was found that the infrared images at the window opening area could reflect the spatial distribution of indoor temperature with an accuracy within 0.5 °C under stable heating and transitional conditions. In the fan coil cooling condition, however, although the infrared image can reflect the cold air flow pattern, the deviations between the infrared temperature and the measured room temperature exceeded 1.0 °C. The effect of window opening grade on the recognition accuracy kept within 0.5 °C.

Enhancing IAQ, thermal comfort, and energy efficiency through an adaptive multi-objective particle swarm optimizer-grey wolf optimization algorithm for smart environmental control

People in modern societies spend most of their time in buildings, and a considerable amount of energy is used to maintain indoor air quality (IAQ) and thermal comfort levels to ensure people's health and productivity. Thus, to ensure the energy efficiency of buildings, smart environmental control must be implemented through prediction and optimization based on artificial intelligence (AI) algorithms. However, rapid and accurate environmental control based on comprehensive consideration of IAQ, thermal comfort, and energy savings remains to be accomplished. Therefore, this paper describes the development and application of a multi-objective AI algorithm that can be deployed in practical environmental control to rapidly and accurately optimize the IAQ, thermal comfort levels, and energy efficiency of buildings. First, a database of indoor airflow and temperature distribution is established through computational fluid dynamics (CFD) simulations and chamber experiments. Then, with the database, the back-propagation neural network (BPNN) model is created to rapidly predict air pollution concentrations and thermal comfort levels. If these predictions using the BPNN model do not satisfy the setup requirements, an adaptive multi-objective particle swarm optimizer-grey wolf optimization (AMOPSO-GWO) algorithm is used to identify the optimal strategy for maximizing indoor air quality, thermal comfort levels, and energy saving. The results demonstrate that the BPNN-based AMOPSO-GWO algorithm has high predictive accuracy (>90%). The mean reduction in air pollution concentrations, increase in thermal comfort levels, and average energy savings are 31%, 45%, and 35%, respectively.

CFD Analysis of Solar Greenhouse Thermal and Humidity Environment Considering Soil–Crop–Back Wall Interactions

In the study of solar greenhouses, microclimate, soil, and back walls have an important influence on the greenhouse thermal environment because of their good heat storage and release characteristics. The transpiration of crops makes indoor humidity increase sharply, which is the main factor affecting indoor humidity distribution. Therefore, it is of great significance to grasp the microclimate change law of solar greenhouses and study the coupling effect of thermal and humidity environment. In this paper, based on computational fluid dynamics (CFD), a three-dimensional model of the thermal and humidity environment of a solar greenhouse is established, and the indoor temperature and humidity distribution under the influence of soil, crops, and back walls are considered. The CFD model initialization uses binary fitting functions to fit the temperature distribution of soil, back wall, and air. The distribution law of the temperature field and relative humidity field of the solar greenhouse under three different working conditions is simulated, that is, the insulation is uncovered and the ventilation window is closed during the day (G1), the insulation is uncovered and the ventilation window is opened during the day (G2), and the insulation is put down and the ventilation window is closed at night. (G3). The results show that the simulation results are in good agreement with the actual results under the three working conditions, and this paper can provide a reference for the improvement of the greenhouse structure and environmental regulation.

Research on New Air Supply Mode of Fan-coil Unit Based on CFD

Fan coil unit air supply is one of the main air supply modes of air conditioning. The fan coil unit system generally adopts the air distribution form of upward supply and upward return on the same side. In this work, a static pressure box is added at the outlet of the fan coil, and a deflector with separate control direction is set at the outlet of the static pressure box. In summer, the combination of up-side air supply and up-attached down-flow air supply is adopted to adjust the deflector to control the distribution of cold air and keep the overall indoor temperature distribution consistent. In winter, the air supply outlet is transferred to the lower part of the fan coil, and the hot air is directly sent to the personnel activity area at the lower part of the room by the adhesive effect of the jet, effectively solving the indoor air stratification phenomenon in winter.