Contribution Ratio Of Indoor Climate Research Articles

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%.

Approach on prediction of indoor temperature distribution by combining POD, CRI and mobile sensors

In our previous study, a prediction method of indoor temperature distribution based on the Contribution Ratio of Indoor Climate (CRI) and finite air temperature collected by one mobile sensor has been proposed. However, the CRI fixed value hypothesis limits its application in practical situations. In this regard, this study proposes to introduce interpolation POD (proper orthogonal decomposition) method to obtain the dynamic distribution of CRI, so as to improve the prediction accuracy. As a case study, in a simplified CFD model with identified heat source conditions, based on the interpolation POD method, the reconstruction of each sub temperature field dominated by only one heat source under any air supply parameters was realized, and thus its CRI distribution could be calculated. Then, by combining the finite air temperature collected by the mobile sensor, the indoor temperature distribution was predicted. The results showed that the introduction of interpolation POD method is effective, which breaks the application limitations of CRI fixed value hypothesis on the proposed method, and further promotes its development potential in the direction of "real-time prediction and dynamic regulation" in practical application.

Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors

In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given.

Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system

Heating, ventilation and air conditioning (HVAC) systems are the most energy-consuming building implements for the improvement of indoor environmental quality (IEQ). We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation, through the fast prediction methods by using low-dimensional linear ventilation model (LLVM) based artificial neural network (ANN) and low-dimensional linear temperature model (LLTM) based contribution ratio of indoor climate (CRI(T)). To be continued for integrated control of multi-parameters, we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model (LLHM) and contribution ratio of indoor humidity (CRI(H)), and thermal sensation index (TS) for assessment. CFD was used to construct the prediction database for CO2, temperature and humidity. Low-dimensional linear models (LLM), including LLVM, LLTM and LLHM, were adopted to expand database for the sake of data storage reduction. Then, coupling with ANN, CRI(T) and CRI(H), the distributions of indoor CO2 concentration, temperature, and humidity were rapidly predicted on the basis of LLVM-based ANN, LLTM-based CRI(T) and LLHM-based CRI(H), respectively. Finally, according to the self-defined indices (i.e., EV, ET, EH), the optimal balancing between IEQ (indicated by CO2 concentration, PMV and TS) and energy consumption (indicated by ventilation rate, supplied air temperature and humidity) were synthetically evaluated. The total HVAC energy consumption could be reduced by 35% on the strength of current control strategies. This work can further contribute to development of the intelligent online control for HVAC systems.

Air conditioning system online control by incorporating low-dimensional linear models and intelligent predictions

Development of control techniques for heating, ventilation and air conditioning (HVAC) systems to provide occupancy driven energy and comfort management has been an active area. In this study, we carried out the work on the balance between indoor environmental quality (IEQ) and energy consumption with low-dimensional linear models (LLM), artificial neural network (ANN) and contribution ratio of indoor climate (CRI) to provide support for HVAC control. In general, ventilation (pollutant) and temperature factors were both considered in the CFD simulation. Linear ventilation model (LVM) and linear temperature model (LTM) were employed to expand the CFD database. Then, combined with ANN, CRI and LLM, LLVM (low-dimensional linear ventilation model)-based ANN and LLTM (low-dimensional linear temperature model)-based CRI could rapidly predict the indoor environmental parameters. The evaluation indices were also defined to provide weightings between indoor environment and energy consumption. On this basis, the HVAC energy consumption caused by ventilation and air conditioning loads could be decreased by 50% and 16% respectively. This study will promote an intelligent HVAC control strategy for user comfort and HVAC energy conservation.

Development and application of linear ventilation and temperature models for indoor environmental prediction and HVAC systems control

It has been of great importance to develop better control techniques for heating, ventilation and air conditioning (HVAC) systems to provide occupancy driven energy and comfort management due to the significance of building energy conservation. Following with our previous work, where low-dimensional linear ventilation model (LLVM) and artificial neural network (ANN) were incorporated to realize online control of indoor air quality (IAQ), we continued to expand the work on indoor thermal comfort (ITC) with low-dimensional linear temperature model (LLTM) and contribution ratio of indoor climate (CRI) to provide dependable support for HVAC online control. Two steps were required to be implemented, respectively considering pollutant and temperature responses. As a premise for control, the database was constructed by CFD, which was verified by the corresponding experiments. Linear ventilation model (LVM) and linear temperature model (LTM) could be well employed to expand the CFD database. Then, combined with satisfying ANN and CRI along with low-dimensional linear model (LLM) for database reconstruction, LLVM-based ANN and LLTM-based CRI could rapidly predict the distribution of indoor environmental parameters (e.g., CO2 concentration and temperature) within acceptable errors. Next, the comprehensive evaluation indices were defined to provide weighting factors for indoor environment (IAQ and ITC) and air conditioning energy. By using the current control strategy, the HVAC energy consumption resulting from ventilation and air conditioning loads could be significantly decreased up to 50% and 32% respectively. This study will further promote an intelligent control strategy to improve the deteriorated condition between occupant comfort and HVAC energy conservation.

非等温車室内の熱伝達性状の把握 （第3報）室内温熱環境形成寄与率(CRI)を用いた温度予測精度の検証

非等温車室内の熱伝達性状の把握 （第3報）室内温熱環境形成寄与率(CRI)を用いた温度予測精度の検証

固定流れ場の熱応答を用いた熱環境シミュレーション : 第3報-室内温熱環境寄与率CRIをネットワークモデルに組み込んだ期間エネルギーシミュレーション

固定流れ場の熱応答を用いた熱環境シミュレーション : 第3報-室内温熱環境寄与率CRIをネットワークモデルに組み込んだ期間エネルギーシミュレーション

Inverse design of underfloor heating power rates and air-supply temperature for an aircraft cabin

Thermal boundary conditions in commercial airliner cabins are crucial for creating a comfortable cabin environment. Cabin temperature distributions depend on the thermo-fluid boundary conditions of the boundary walls and the air supply. This paper proposed a combined inverse-forward model for designers to determine the total underfloor heating rates and the air-supply temperature in an aircraft cabin. The contribution ratio of indoor climate (CRI) is applied to describe the cause–effect relationship between the boundary wall convective heat release rates and the resulting temperature rise at certain points, which can be cast into a matrix. The solution contains three sub-models: (i) regularized inversion of the cause–effect matrix with the target cabin air temperatures as the known input, which solves the convective heat rates of the underfloor heaters and the air-supply temperature, (ii) solution of underfloor heater's surface temperatures based on Newton's law of cooling, and (iii) computation of the radiative heat rates. The above model was used to determine the total underfloor heating rates and air-supply temperature in a single-aisle aircraft cabin. The design targets are to create an average temperature of 24 °C near the upper human body and 26 °C at the ankle level. An experimental test was conducted in a simplified half section of an aircraft cabin for model validation. The results show that the proposed methodology is able to provide the total underfloor heating rates and air-supply temperature in good agreement with the measurement data and forward-simulation boundary conditions.

Inverse determination of wall boundary convective heat fluxes in indoor environments based on CFD

The boundary convective heat transfer of air-conditioned rooms has a large effect on the indoor thermal environment. To design a comfortable thermal environment, the unknown wall boundary convective heat fluxes must be determined. Current inverse models that determine the required thermal boundary conditions must use an iterative guess-and-correct procedure, which is quite time-consuming. This study proposes an inverse method based on Tikhonov regularisation and least-squares optimisation using computational fluid dynamics (CFD) to determine the wall boundary convective heat fluxes in indoor environments. The wall convective boundary heat fluxes can then be solved by inverse matrix operations with discrete target temperatures at distinct points. To accelerate the solving procedure, the contribution ratio of indoor climate (CRI) is applied to describe the cause–effect relation between the wall convective heat fluxes and the resulted discrete temperatures at certain points. This study finds that the developed inverse method can accurately and efficiently determine the wall convective heat fluxes. The only prerequisites to implement the proposed inverse method are a fixed flow field and the given target temperatures at certain points in space.

Building energy simulation considering spatial temperature distribution for nonuniform indoor environment

Building energy simulations are mostly implemented using network or multi-zone models that do not consider indoor air temperature and velocity distribution. However, the recent introduction of personal ventilation, floor-heating systems, and displacement ventilation positively utilizes a nonuniform indoor environment to meet the demand for both energy efficiency and thermal comfort. Therefore, it is expected that building energy simulation results will be significantly impacted by the choice of an appropriate reference air temperature for the calculation of heat transfer through constructed materials. This means that the air temperature distribution needs to be taken into account for nonuniform environments when carrying out energy simulations. An alternative approach to this problem is to combine a computational fluid dynamics (CFD) simulation directly with a network model; however, this approach is unfortunately too computationally time-consuming. In this study, we propose an acceptably fast simulation method that couples the contribution ratio of indoor climate (CRI), which is extracted from CFD results and indicates the individual impact of all heat factors, with the network model to implement an energy simulation that incorporates a temperature distribution. With the introduction of CRI, it is possible to achieve a precision as high as that of CFD and a calculation speed as fast as that of the network model. A case study simulating the thermal load of a single office room was carried out with the CRI-coupled method. The energy demand result calculated by CRI-coupled method was 15–20% lower than that of a non-coupled network simulation.

Periodic building energy simulation by coupling the contribution ratio of indoor climate (CRI) with network model

Periodic building energy simulation by coupling the contribution ratio of indoor climate (CRI) with network model

Optimization of Sensor Location using Contribution Ratio of Indoor Climate

Optimization of Sensor Location using Contribution Ratio of Indoor Climate

Development of new indices to assess the contribution of moisture sources to indoor humidity and application to optimization design: Proposal of CRI (H) and a transient simulation for the prediction of indoor humidity

Many indoor and outdoor factors (e.g., the presence of occupants, hot-water supply equipment, the use of hygroscopic materials, and ventilation) contribute to indoor humidity. It is important to investigate and understand the contribution of each factor to indoor humidity and to establish an effective method for the design and control of indoor humidity. In this study, indoor humidity was treated as a linear summation of the contribution of various factors, all of which can cause an increase or decrease in indoor humidity. New indices for assessing the contribution of factors to the humidity distribution in a room are proposed as Contribution Ratios of Humidity (CRI (H)) 1, 2, and 3 which can be calculated based on Computational Fluid Dynamics (CFD) simulations. Furthermore, a transient simulation based on CRI (H)1 and the Contribution Ratio of Indoor Climate (CRI (C)) was developed to predict the indoor humidity distribution. A 100-day transient analysis was performed in a living room in which moisture-buffering materials were used. The simulation results were compared with those from a well-mixed zonal model and a CFD transient analysis to confirm the effectiveness of the approach. The analysis provided the three-dimensional spatial distribution of indoor humidity and temperature with good prediction accuracy. The calculation time was approximately equal to that of the well-mixed zonal model and much faster than that of the CFD transient analysis.

APPLICATION OF CONTRIBUTION RATIO OF INDOOR CLIMATE (CRI) IN ANALYSIS OF HEAT TRANSFER IN THE ROOM DOMINATED BY NATURAL CONVECTION

The contribution ratio of indoor climate (CRI) based on CFD was developed to estimate the individual contribution of heat factors to any location inside a room. With the index of CRI, a more precise building energy simulation or real-time systematic control can be expected, within the defined accuracy by means of CFD but with a much reduced calculation load. However most of the studies related to CRI involve forced convection airflow fields. Thus the purpose of this study is to use the CRI to analyze the characteristics of heat transfer in natural convection air flow fields. In this study, A CFD simulation coupled with convection and radiation has been performed in an atrium without air conditioning or ventilation. Based on the fixed air flow field, the CRI of all heat sources are calculated by the new definition of CRI. As an application of CRI, an approximation method is proposed in this study to discuss the sensitivity of each heat source and the heat transfer by convection and radiation respectively. The accuracy of predicting air temperature by CRI is examined by comparing with CFD analysis.

Calculation Method and Application of Contribution Ratio of Indoor Climate (CRI) by Means of Setting a Uniform Heat Sink in Natural Convection Air Flow Field

Calculation Method and Application of Contribution Ratio of Indoor Climate (CRI) by Means of Setting a Uniform Heat Sink in Natural Convection Air Flow Field

Control of indoor thermal environment based on concept of contribution ratio of indoor climate

The contribution ratio of indoor climate (CRI) derived from computational fluid dynamics (CFD) was developed to estimate the individual contribution of heat factors to any location inside a room. The CRI index indicates the structure of the temperature field and allows the CFD results to be applied to analysis and design, with more efficient application. In this study, the concept and calculation method for CRI is introduced first. As an example of the application of CRI, a method for predicting temperatures at any point in a room with a small number of temperature sensors based on CRI is developed. The accuracy of the method is examined by comparing the prediction with a coupled simulation of CFD and radiation.

CALCULATION METHOD OF CONTRIBUTION RATIO OF INDOOR CLIMATE (CRI) BY MEANS OF SETTING A UNIFORM HEAT SINK IN NATURAL CONVECTION AIR FLOW FIELD

The contribution ratio of indoor climate (CRI) based on CFD was developed to estimate the individual contribution of heat factors to any location inside a room. With the index of CRI, a more precise building energy simulation or real-time systematic control can be expected, within the defined accuracy by means of CFD but with a much reduced calculation load. However most of the studies related to CRI involve forced convection airflow fields. Thus the purpose of this study is to make the CRI effective also in natural convection air flow fields. In this study, a new calculation method was proposed by means of setting a uniform heat sink, by which the steady calculation of CRI could be achieved. A CFD simulation coupled with convection and radiation has been performed in an atrium without air conditioning or ventilation. With the new method the CRI of the north wall was calculated and its influence to the air temperature at some representative locations was analyzed as an example. In addition the time response of CRI was also discussed in this paper.

COMPARATIVE ANALYSIS OF THE COUPLED SIMULATION OF CFD AND ENERGY SIMULATION AND UNIFORM-MODEL-BASED SIMULATION

We proposed how to predict the thermal environment easily and quantitatively according to the change in the heat load by CRI (Contribution Ratio of Indoor climate). And, we developed energy simulation considering spatial distribution, by coupling the thermal predict equation with CRI and Energy simulation, such as thermal network calculating program. In this paper, we compare this mathematical evaluation method, CRI-ES model, with conventional method, uniform-model-based simulation, through the case study of various size of room. And we find the flow field is influenced by the exhaust outlet especially in a large size room and it has a great impact on the result of the energy consumption.

TEMPERATURE AND CONTAMINANT CONCENTRATION DISTRIBUTION AND ENERGY SAVING INSIDE AN OFFICE ROOM WITH HYBRID AIR-CONDITIONING SYSTEM WITH WIND-FORCED VENTILATION

In this paper, the influence of combination of the introduction method of wind-forced ventilation and the supply method of conditioned air on the indoor thermal environment and airflow characteristics are examined by means of CFD calculation. In CFD, two types of supply location of the air conditioned by AHU were set : (1) supply from ceiling, (2) supply from floor, and four locations of outdoor air intake for wind-forced ventilation were set ; (1) ceiling, (2) high position of outer wall close to ceiling, (3) low position of outer wall close to floor and (4) floor. From the calculated data, indoor environment and energy efficiency were evaluated by the distribution of air temperature, contribution ratio of indoor climate No.3 (CRI3) and CO2 concentration emitted from occupants.