Prediction Of Thermal Environment Research Articles

Analysis of asymmetric thermal sensation under the coupling effect of temperature difference in multiple parts of human body

Responding to thermal comfort based on physiological indicators is a broad and reliable research method. How to use skin temperature to predict thermal comfort in a non-uniform environment is of great significance. 24 volunteers were selected for testing with local radiant heating in Kitakyushu, Japan. It was found that the most obvious skin temperature difference value occurred between left and right thighs while heating after 30 min, which would exceed 5 °C. The asymmetric thermal sensation votings (ATSV) and the skin temperature difference were significantly influenced while changing radiation intensity and radiation distance, the ATSV of the subjects in the small space was about 0.3 lower than that of the subjects in the large space. Increasing the radiation intensity, the TSV of the small space decreased by about −0.5, which can be seen that higher radiation intensity may bring lower value of the TSV. A prediction model of the ATSV and skin temperature difference was established and the sensitivity analysis was performed on the different body parts that significantly affected the ATSV, and the dominant factors affecting the ATSV were obtained. This paper complements the non-uniform thermal environment prediction method.

A thermal environment prediction method for a mine ventilation roadway based on a numerical method: A case study

Thermal environment prediction has become increasingly significant in recent years with its promise of wide application in underground structures. The current work presents a numerical method for the feasible and effective analysis of the airflow and surrounding rock temperatures in ultralong mine ventilation roadways to understand their dynamic heat transfer. To reduce the modeling effort and computation time with satisfactory accuracy, the ventilation roadway is approximated as a one-dimensional (1D) line element. Still, the surrounding rock of the roadway remains three-dimensional (3D). An equivalent heat transfer coefficient calculates the dynamic heat transfer in the radial direction of the roadway. A case analysis of the Sanhejian coal mine ventilation roadways in China is performed, and a comparison between the simulated results and field measurements indicates that the predicted airflow temperature shows good agreement. The change in the underground thermal environment concerning ventilation time is systematically investigated in detail. The surrounding rock of the roadway adjusts the underground thermal environment through heat absorption or heat release, and the temperature distribution of the surrounding rock presents a "V" shape in winter and a "W" shape in summer. The self-compression of air contributes to a remarkable heat source for the increase in airflow temperature in the air intake shaft. A roadway with a low initial temperature of the surrounding rock after long-term ventilation can effectively cool the high-temperature airflow underground in early summer. The reduction in ventilation volume will increase the cooling degree of airflow by the surrounding rock in long-term ventilation roadways in summer. Still, the airflow will be heated more significantly in short-term ventilation roadways. In addition, the increase in the average annual ambient temperature will result in a linear rise in underground airflow temperature.

Spatial prediction of outdoor thermal comfort using an operational NWP model for city and regional scales: A case study in Korea

The outdoor thermal environment has key implications for human health, and numerical simulation models have been used to predict thermal comfort. The potential of a numerical weather prediction (NWP)-based thermal environment prediction model has not yet been comprehensively explored. Using a predicted mean vote-based thermal comfort index (i.e., perceived temperature [PT]) in South Korea, this study developed and evaluated a thermal environment prediction model using an operational NWP model of the Korea Meteorological Administration. The suitability of four bias-adjustment methods to reduce bias in PT prediction was examined, applying the outputs of the NWP model using in-situ data for 2018–2019. Bias-adjusted PT prediction performance was assessed for 2020 based on PT and thermo-physiological stress categories for South Korea. The predicted PT successfully represented PT spatial information. Mean absolute errors (MAEs) were 2.17 and 1.15 °C for hourly and daily PT, and the bias-adjustment method improved prediction accuracy by 18 and 36%, respectively, based on the MAE. PT prediction via NWP modeling can be further improved by reducing the statistical error in surface solar radiation modeling. The proposed scheme can be applied in urban heat mitigation, developing early warning systems for thermal health risks, energy consumption prediction, and tourism planning.

Fast prediction model of three-dimensional temperature field of commercial complex for entrance-atrium temperature regulation

Based on the status quo of poor winter thermal comfort of commercial complexes, we developed a three-dimensional temperature prediction model for the typical space of commercial buildings, the “Entrance-Atrium”. A database of 4500 samples was built by using computational fluid dynamics to simulate different spatial deformations and wind environments based on typical models. The simulation method was verified by on-site measurement. Two indicators, Cr (Compliance rate of temperature) and Tr (Temperature resistance index), were defined to evaluate the thermal environment performance of the spatial form. And S (spatial performance score) was used for the comparison of architectural design schemes. In order to achieve rapid temperature prediction, the simulation database was reconstructed and expanded by artificial neural network algorithm, and then the three-dimensional temperature prediction model was constructed. The prediction model may be applied for thermal environment prediction and architectural design optimization for key spaces. Meanwhile, we have expanded its application in air conditioning supply volume adjustment and verified its reliability through practical cases. This study may provide a reliable basis for architects and engineers to make critical decisions, helping optimize the thermal environment through a combination of active and passive methods in the entire process from building design to operation, and improve the utilization efficiency of building operation energy consumption.

Dual memory scale network for multi-step time series forecasting in thermal environment of aquaculture facility: A case study of recirculating aquaculture water temperature

Multi-step time series forecasting is essential in engineering. However, effective time series prediction of the agricultural environment is still a challenge due to the disturbance of external factors and the complexity of temporal patterns. The objective of this work is to provide an intelligent prediction system with field priori-knowledge for aquaculture facility thermal environment. Specifically, we present a novel network framework that considers the spatial correlation of exogenous environmental factors, the short-term and long-term temporal dependence of the sequence, and the spatio-temporal fusion correlation at different times. Among them, the dual-scale memory components focus on long-term seasonal and trend memory patterns as well as short-term fluctuation memory patterns, respectively. Besides, the attention mechanism coordinates the information flow between the long and short memory components. We applied the model to recirculating water temperature forecasting in the aquaponics greenhouse. The empirical results of global and local metrics indicate that our proposed model outperforms benchmark models. The RMSE of the proposed dual memory scale network (DMSNet) is 0.1631, 0.3206, and 0.3761, respectively, in the next 6 h, 12 h, and 24 h water temperature prediction. Ablation study proved that the components of our proposed network could be targeted to extract different information patterns from aquaponics environmental sequence data. Experimental results show that the thermal environment prediction based on DMSNet has higher precision in short-term and long-term forecasting multi-step prediction.

Vertical temperature distribution in large space buildings in the heating season: On-site measurement and Block-Gebhart model prediction considering air infiltration

In the heating season, the air infiltration in large space buildings cannot be neglected, which will cause a complex movement of indoor air and affect the creation of the thermal environment. Existing thermal environment prediction models often treat the indoor space as a whole, and cannot consider the temperature gradient in the vertical direction. Therefore, in this investigation, based on the Block-Gebhart (B-G) model, combined with the air infiltration model, the indoor air temperature distribution and infiltration air volume in large space buildings were predicted. The on-site measurement was carried out in an engineering training plant in Shanghai, which used nozzle air supply as a stratified environment regulation method. The measurement results verified the accuracy of the indoor thermal environment predicted by the compound model. The influence of the outdoor temperature, the supply air temperature and velocity, and the height difference of the infiltration and exfiltration air outlets on the indoor air temperature and the infiltration air volume was analysed. The infiltration air volume was most significantly affected by the height difference. In addition, the influence of the above factors on the thermal comfort of the personnel activity area was discussed. In general, the proposed compound model can accurately predict the indoor thermal environment under air infiltration and provide a theoretical basis for the design of airflow distribution in large space buildings in winter.

데이터센터 열환경 예측 및 적응형 제어알고리즘 개발

Purpose: As cooling energy accounts for 50.5% of the data center total energy use, it is necessary to reduce cooling energy with optimal control method. Thus, this study aimed at developing an adaptive control algorithm for data center thermal environment based on Artificial Neural Network (ANN). Method: A data center thermal environment model was built to obtain variable data which is used to train and evaluate performance of the prediction model and algorithm. The thermal environment prediction model was developed using ANN and optimization process was conducted by the Bayesian Optimization Algorithm. The adaptive control algorithm, which embedded the prediction model, adopted the Sliding Windows method and was optimized to maximize the control performance. Result: The performance evaluation of the developed algorithm was conducted compared with non-adaptive algorithm. As a result, the adaptive algorithm presented better performance than the non-adaptive with 0.45 of RMSE. Therefore, the developed algorithm secured the stability and accuracy and will be applied to supervisory control platforms.

Influences of radiation on the prediction of thermal environment in a transient buoyancy-driven natural ventilation classroom

Computational fluid dynamics (CFD) simulations are performed to reveal the significant effects of radiation on predictive accuracy of thermal environment in a transient buoyancy-driven natural ventilation classroom. The present numerical method is verified by the experimental results. The time evolutions of airflow field, radiation heat flux, buoyancy force, and airflow rate are analyzed in detail. In addition, the effects of radiation on thermal comfort are evaluated by three different indices. It is found that radiation plays a critical role in the establishment of the indoor thermal environment. The proportion of radiation heat flux increases with the effective radiation area but decreases with the power of the occupants. Finally, the indoor space can be divided into five different occupied zones depending on how much radiated heat the occupants emit. Radiation can change the indoor heat flux distribution, directly leading to a smaller buoyancy force and ventilation rate. As a result, the thermal comfort except for any draft is linearly affected by both the power and the effective radiation area, and the greater the proportion of radiation heat flux is, the better the indoor thermal comfort is. These conclusions demonstrate that radiation has a noticeable impact on the dynamic evaluations of indoor thermal environment.

Model experiment with spatial resolution enhancement for verification of thermal environment prediction in tunnel

Model experiment with spatial resolution enhancement for verification of thermal environment prediction in tunnel

Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network

With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station.

Prediction of Thermal Environment in a Large Space Using Artificial Neural Network

Since the thermal environment of large space buildings such as stadiums can vary depending on the location of the stands, it is important to divide them into different zones and evaluate their thermal environment separately. The thermal environment can be evaluated using physical values measured with the sensors, but the occupant density of the stadium stands is high, which limits the locations available to install the sensors. As a method to resolve the limitations of installing the sensors, we propose a method to predict the thermal environment of each zone in a large space. We set six key thermal factors affecting the thermal environment in a large space to be predicted factors (indoor air temperature, mean radiant temperature, and clothing) and the fixed factors (air velocity, metabolic rate, and relative humidity). Using artificial neural network (ANN) models and the outdoor air temperature and the surface temperature of the interior walls around the stands as input data, we developed a method to predict the three thermal factors. Learning and verification datasets were established using STAR CCM+ (2016.10, Siemens PLM software, Plano, TX, USA). An analysis of each model’s prediction results showed that the prediction accuracy increased with the number of learning data points. The thermal environment evaluation process developed in this study can be used to control heating, ventilation, and air conditioning (HVAC) facilities in each zone in a large space building with sufficient learning by ANN models at the building testing or the evaluation stage.

シミュレーションによるスマートハウスの温熱環境とエネルギーの予測 （その1）標準的な住宅の評価モデルの構築

シミュレーションによるスマートハウスの温熱環境とエネルギーの予測 （その1）標準的な住宅の評価モデルの構築

C-3 地下街温熱環境予測に関する研究

C-3 地下街温熱環境予測に関する研究

A-30 設計プロセスの階層構造を考慮した最適空調設計支援システム(その2) : CRIによる温熱環境予測と冬期空調条件の最適化

A-30 設計プロセスの階層構造を考慮した最適空調設計支援システム(その2) : CRIによる温熱環境予測と冬期空調条件の最適化

A-14 床暖房による室内熱環境の数値予測 : 数値予測と実測との比較例

A-14 床暖房による室内熱環境の数値予測 : 数値予測と実測との比較例

Numerical prediction of thermal environment and energy consumption of three different greenhouses under hot and semi-arid climate

The evaluation of the thermal performance of a greenhouse requires an understanding of the energy exchanges between its components. In recent years, Computational Fluid Dynamics (CFD) proved to be efficient to predict the microclimate inside greenhouses, but few CFD works analyzed hot and arid climatic conditions. The aim of the present study is to investigate the impact of such specific conditions prevailing in the southern Mediterranean basin considering three different unheated greenhouses equipped with rows of canopy (tunnel, Venlo and plastic vertical wall greenhouse). 2D simulations were conducted in order to estimate the influence of the greenhouse geometry and cover material on the energy balance, focusing in particular on the heat losses. To establish the boundary conditions and validate the model, experiments were launched inside the greenhouses. Simulations were conducted for two contrasted conditions: the nocturnal period when the energy performance of each type of greenhouse was investigated, and the diurnal period for which a solar day was simulated taking account of natural ventilation. A good agreement was found with experiments concerning the temperature profiles and the energy losses. The predicted stable flow patterns slightly differed from one greenhouse to another for each condition: during the night they all disclosed two convective loops sweeping the floor, rising along the middle of the greenhouse, cooling down along the roof and moving down along the sidewalls of the greenhouse, while during the day flow patterns were strongly impacted by the vent openings. The obtained values (temperature and velocity) were coherent with results found in the literature for different greenhouse types and sizes. The developed tool offers potential applications in research on greenhouse climate to assess different cover material performances. It could also be used to investigate the energy consumption and partition for different greenhouse shapes, different crop densities different growth stages.

トンネル内温熱環境予測手法検証のための模型実験

トンネル内温熱環境予測手法検証のための模型実験

A heatstroke prediction and prevention system for outdoor construction workers

Abstract Background Recently, the number of heatstroke cases is increasing among construction workers. To prevent heatstroke at construction sites, it is necessary to accurately predict both the thermal environment of construction sites and the physiological condition of workers, which is presently difficult to achieve. Methods We have therefore developed the Construction Workers’ Heatstroke Prevention (CWHP) system specifically for outdoor construction workers. The CWHP system consists of the Thermal Environment Prediction (TEP) system, which predicts changes in the thermal environment based on forecast values obtained from the Japan Meteorological Agency and results of computational fluid dynamics and solar insolation simulations, and the Core Body Temperature Prediction (CBTP) system, which predicts changes in worker core body temperature based on the TEP system results. The CWHP’s heatstroke risk notification system indicates the risk of worker heatstroke considering the work site and schedule, so that construction managers can appropriately schedule work or install appropriate facilities such as tents, electric fans, and cold water tanks before work starts. The system is flexible enough to accommodate situations differing from meteorological agency forecasts. Results In summer 2011 the CWHP system was applied at Osaka University campus under hypothetical situations using the Virtual Reality Avatar Thermal Environment (VRATE) system, previously developed by the authors. Conclusions The system accurately predicted the time history of construction worker core body temperatures and informed users of times of heatstroke risk, allowing work sites and worker schedules to be modified such that new plans showed no risk for that day.

Numerical Prediction of Thermal Environment and Energy Consumption in Tunnel-Type Greenhouse with Supplementary Air-mixing and Heating Device

Owing to the fact that the growing of agricultural crops can be directly affected by climate conditions, large-scale greenhouses are usually used to control the indoor climate conditions for cultivation in agriculture. The envelope of greenhouses generally consists of a polyvinyl chloride (PVC) sheet, which produces a so-called greenhouse effect and protection for crops from harmful insects. The research concerning indoor environmental conditions formed in greenhouses and precise numerical predictions of airflow and temperature distribution in large-scale greenhouses are important to develop effective designs from the viewpoint of energy-saving and the development of suitable indoor environments for agricultural products. To this end, this paper presents a numerical analysis of flow and temperature distributions in greenhouse and the effect of an air circulation fan for mixing air in the greenhouse. The uses of a PVC curtain inside the greenhouse and feedback control of supply air temperature were evaluated by coupled numerical analysis of Computational Fluid Dynamics. From the viewpoint of heat load of the supplementary heating device, the energy-saving effects of about 10–30% were confirmed upon introduction of a PVC curtain and air-mixing fan in a large-scale PVC greenhouse. The sensor position in the greenhouse was confirmed to have a great impact on energy consumption.

Coupled simulations for naturally ventilated rooms between building simulation (BS) and computational fluid dynamics (CFD) for better prediction of indoor thermal environment

The coupling strategies for natural ventilation between building simulation (BS) and computational fluid dynamics (CFD) are discussed and coupling methodology for natural ventilation is highlighted. Two single-zone cases have been used to validate coupled simulations with full CFD simulations. The main discrepancy factors have also been analyzed. The comparison results suggest that for coupled simulations taking pressure from BS as inlet boundary conditions can provide more accurate results for indoor CFD simulation than taking velocity from BS as boundary conditions. The validation results indicate that coupled simulations can improve indoor thermal environment prediction for natural ventilation taking wind as the major force. With the aids of developed coupling program, coupled simulations between BS and CFD can effectively improve the speed and accuracy in predicting indoor thermal environment for natural ventilation studies.