Supply Air Temperature Research Articles

Experimental evaluation of energetic and exergetic enactment for exotic evaporative to expansion-type edifice-coolers

Evaporative cooling is a natural, environment-friendly, and economical method of air-conditioning involving minimal energy consumption. However, the wider dependency on the climatic condition, lower cooling capacity, lesser effectiveness, excess humidification, etc., limits the evaporative coolers' global acceptance. Therefore, multi-staging of evaporative coolers or hybridization with any conventional direct expansion system is recognised as a solution for building air-conditioning. Accordingly, a three-stage cooling test rig comprising indirect and direct evaporative coolers followed by a direct expansion system is indigenously developed and efficacy of seven single to multi/hybrid modes is experimentally evaluated. Rigorous experiments are conducted during the entire summer of Hyderabad, India, from April to July of 2019 by operating the test rig from 20th to 26th of each month from 8:00 AM to 8:00 PM with an interval of one hour. Based on the acquired experimental data, energy, exergy, heat transfer and environmental analyses are carried out. The three-stage cooling combination exhibits the highest wet-bulb effectiveness reaching a maximum of 300% with the lowest supply air temperature of 16 °C. Multi-staging provides an average sensible cooling load reduction ranging from 15.21% to 65.04% on the direct expansion cooling system. Moreover, the two-stage indirect-direct evaporative cooler exhibits excellent exergy efficiency with sufficient cooling capacity reaching 90%. The DEC-DX, IEC-DEC and IEC-DEC-DX cooling combinations exhibit superior SEER magnitudes over entire cooling season indicating effectiveness of evaporative pre-cooling.

Evaluating the impact sequences of operation have on the implementation of occupant-centric controls

Partial occupancy of commercial offices has become the norm in the wake of the COVID-19 pandemic. Given this, occupant-centric control (OCC), which adapt building systems based on occupants’ presence or preferences, offer an alternative to traditional control that assumes full occupancy. However, poor sequences of operation can degrade the benefits of OCC. This paper explores this interaction by examining energy data from two buildings – one with two control logic faults corrected and an occupancy-based ventilation OCC implemented in 2020, and one with traditional ventilation – from 2019 to 2020. Sequences that impacted implementation in the first building are discussed. Then, a calibrated energy model of the second building is developed to evaluate how occupancy-based ventilation alongside changes to the sequences of operation – namely supply air temperature (SAT) reset and economizer high limits – impacted energy use. The inclusion of OCC and improved sequences in the second building saved 30.6% and 9.6% of annual heating and cooling energy, respectively. Without an SAT reset, OCC saved 4.4% and 3.9% of heating and cooling, respectively, compared to 15.7% and 5.7% when an SAT was present. These results begin to characterize the relationship sequences of operation and OCC implementations have with one another in commercial offices.

Demonstration of an MPC framework for all-air systems in non-residential buildings

In educational buildings, the HVAC system is a major contributor to the total energy use. It is well-known that energy reductions could be achieved by more advanced control strategies for HVAC systems. A model predictive control (MPC) strategy could be a viable solution. Contrary to the standard rule based control (RBC), the predictive control can adjust in the supply air temperature, airflow rate prior to the start of a lecture. This study aims to develop and demonstrate an MPC framework for an all-air system that controls both the room temperature and the CO2-concentrations. To predict these variables two model identification techniques were compared: auto-regressive with exogenous input (ARX-MPC) linear model and a grey-box resistor–capacitor nonlinear model (RC-NMPC). In addition, a cost function that minimized the energy use while guaranteeing thermal comfort and indoor air quality (IAQ) was formulated. This study showed that the ARX-MPC was preferred over the RC-NMPC given its simplicity. As for the model identification, the ARX model was less computationally time-consuming than the grey-box model. Furthermore, the RC-NMPC required more computational time than the ARX-MPC, since the RC-NMPC required a non-linear solver while the ARX-MPC used a linear one. The developed MPC framework was than implemented through BACnet in a real-use educational building to test its actual performance. The energy savings calculated for the implemented MPC strategies with an occupancy based heating set-point were respectively 10%–40% for electric energy use and 21%–55% for thermal energy use.

Classification of sequencing logic faults in multiple zone air handling units: A review and case study

This paper classifies common sequencing logic faults in multiple zone variable air volume (VAV) air handling unit (AHU) systems by conducting a critical review of the literature. Six broad categories of sequencing logic faults are identified affecting the state of operation, mode of operation, and supply air temperature and duct static pressure setpoint reset programs. A case study with building automation system (BAS) trend data from two VAV AHU systems is conducted to provide examples of these sequencing logic faults. Four of the six fault types are found present in at least one of the two AHUs. The most significant fault discovered was an incorrect reference to a nonexistent VAV terminal damper address forcing an unnecessarily high duct static pressure setpoint and wasting fan electricity. The BAS programs are reviewed in consultation with the operations staff, and the coding mistakes leading to the detected anomalous behavior are identified.

New Optimal Supply Air Temperature and Minimum Zone Air Flow Resetting Strategies for VAV Systems

Buildings account for a large portion of the total energy use in the US; therefore, improving the operation of typical variable-air-volume (VAV) systems in buildings can provide a tremendous economic opportunity. ASHRAE Guideline 36 recommends a resetting strategy for supply air temperature (SAT) for VAV systems based on outside air temperature. However, this strategy may not produce optimal performance, particularly when simultaneous cooling and heating occurs in zones. In addition, there is no strategy recommended in the Guideline to reset the zone minimum airflow set point in a single-duct VAV terminal unit with reheat, although this setpoint has a great impact on zone reheat requirements and ventilation efficiency. Thus, this paper introduces new strategies to reset both the SAT and zone minimum airflow rate set points to improve the efficiency of typical VAV systems. The strategies were tested under various conditions through experiments performed in fully instrumented VAV systems located in the HVAC lab at the University of Cincinnati. The experiments were conducted on a chilled-water VAV system that serves three controlled zones with hot-water reheat VAV boxes controlled by a typical commercial BACnet web-based building automation system BAS. The simulation studies were performed using the building energy simulation software EnergyPlus to evaluate the strategies at a larger scale in various locations. The simulation results show that the proposed resetting strategies can provide fan energy savings between 1.6% and 5.7% and heating load savings between 7.7% to 33.7%, depending on the location. The laboratory testing shows that the proposed strategies can provide stable control performance in actual systems as well as achieving the anticipated reheat and fan energy savings. The result offers significant improvements that can be implemented in the Guideline for single-duct VAV system operation and control.

Performance comparison of different flow arrangements of 4-fluid internally-cooled liquid desiccant dehumidifiers

In this study, the performance of 10 different flow arrangements of 4-fluid internally-cooled liquid desiccant dehumidifiers were compared. The four fluids are supply air, exhaust air, liquid desiccant, and water. The comparison was performed using a two-dimensional heat and mass transfer model of the dehumidifier that was solved numerically. The model’s predictions of supply air outlet humidity ratio matched experimental measurements within 6.7%. The two-dimensional variation of the air temperature and humidity ratio in the supply channel showed the importance of using a two-dimensional heat and mass transfer model when at least one of the fluids is in cross-flow with the other fluids. Moreover, a sensitivity analysis was performed to evaluate the effect of nine input parameters (supply air temperature and humidity ratio, exhaust air temperature and humidity ratio, liquid desiccant temperature, concentration, and flow rate, supply air mass flow rate, and exhaust to supply air mass flow rate ratio) on the performance of the dehumidifiers. The results showed that the best performance, in terms of the supply air humidity ratio and enthalpy decrease, was obtained when the supply air was in counter-flow with the exhaust air, liquid desiccant, and water. While the poorest performance was obtained when the supply air was in parallel-flow with the exhaust air and in counter-flow with the liquid desiccant and water. The approximate difference between the best and poorest performing flow arrangements in terms of the decrease in supply air humidity ratio and enthalpy is 4.3% and 10.5%, respectively. The results of the sensitivity analysis showed that for the 10 flow arrangements, the liquid desiccant inlet temperature, and flow rate have the least effects on the performance of the dehumidifier.

BPNN-based optimal strategy for dynamic energy optimization with providing proper thermal comfort under the different outdoor air temperatures

An efficient method is used to optimize indoor air distribution in heating systems of stratum ventilation to be beneficial to develop a potential of energy-saving with providing proper thermal comfort. To save much energy with providing proper thermal comfort, asearch for good optimization methods or strategies is required to optimize indoor air distribution affected the parameters (i.e., supply air temperature, supply vane angle, clothing insulation and outdoor air temperature) in stratum ventilation systems. In this study, the air distribution of stratum ventilation for heating is optimized by using the strategy 1 (the optimal strategy of the predicted mean vote (PMV) between −0.7 and 0.7 based on a technique of order preference by similarity to ideal solution (TOPSIS)) and the strategy 2 (the optimal strategy of PMV between −0.7 and 0.7, and draft rate (DR) less than 20). Firstly, the Computational Fluid Dynamics software is used to perform ventilation operations on the parameters for stratum ventilation systems. Secondly, according to the simulation results, the parameters are set as the input variables to construct the predictive model of ventilation performance (i.e., Energy consumption, PMV and DR) by using the Back Propagation Neural Network (BPNN) method and response surface model (RSM) method, respectively. According to the accuracy analysis of models, the BPNN method is chosen to build the predictive model. Finally, the ventilation performance is output through the optimization of the proposed BPNN-based strategy 1 and strategy 2. The optimized result shows that BPNN can build prediction models of great precision for ventilation performance. The proposed strategy 2 can save more energy by providing thermal comfort than the strategy 1. And the strategies can be beneficial to save energy as the outdoor air temperature varies. Moreover, the clothing insulation instead of supply air temperature indirectly saves energy, while providing proper thermal comfort.

Investigating thermal environment inside the kitchen of air‐conditioned pantry car on Indian Railway using CFD

AbstractIn an indoor kitchen environment, proper installation of supplying air system is essential for maintaining optimal human thermal comfort level. Current thermal comfort investigation has been accomplished during “various cooking periods (breakfast, lunch, snacks, dinner)” inside the air‐conditioned kitchen environment of railway pantry car in India. The study goals to improve thermal comfort inside the pantry kitchen by implementing proper installation of the supply air system. A field experiment and computational fluid dynamics approach has been carried out during the summer season. Also, standard effective temperature (SET) index was utilized to determine the thermal comfort status. Three proposed design modified cases of pantry cars (based on supplying air system concepts) have been executed followed by validation and comparison with the existing case model. Results revealed modified Case III as a better design concept (indicating better air supply circulation and temperature decrease at the desired location) compared to the existing and other two proposed cases. Simultaneously, the SET index range was 26.5–28.6°C; indicating “comfortable thermal sensation throughout the cooking period.” This study's outcomes suggest improvements in thermal comfort and energy savings of “air‐conditioned” pantry car kitchens in “Indian Railways” that could be referred directly.

Experimental Study on the Influence of Solar Heat Gain on the Thermal Performance of Hollow Ventilated Interior Wall

Abstract The heating system combining solar air collector with hollow ventilated interior wall (SAC-HVIW) can effectively extend the heating time. However, due to the large wall-window ratio of buildings on Tibetan Plateau, the strong solar radiation irradiating on the interior wall may influence the thermal performance of HVIW. In this paper, an experimental room is constructed to study the influence of external solar heat gain on the thermal performance of HVIW. Steady-state measurements are carried out by considering different ventilation rates, supply air temperatures and heat gains. Results show that the external heat gain has almost no effect on U-value, but it increases the heating capacity by increasing the logarithmic mean temperature difference (LMTD). For all cases, the heating capacity of HVIW is related to LMTD and supply air velocity, and U-value mainly increases with supply air velocity. Heat transfer of the interior surface of HVIW is dominated by forced convection which increases linearly with supply air velocity. The radiant heat transfer coefficient of the exterior surface of HVIW is not affected by the external heat gain with the mean value of 5.65 W/(m2 · K), while the convective heat transfer coefficient increases logarithmically with the external heat gain. The proportion of radiant heat transfer decreases as a power function with the increase of the exterior surface temperature. Measurements in this paper are used to evaluate the influence of external heat gain on the heating performance of HVIW, which is beneficial to the design of HVIW.

Modeling the effect of dual-core energy recovery ventilator unit on the energy use of houses in northern Canada

The use of single-core energy recovery ventilator (ERV) reduces the heating energy use in northern houses, but has the disadvantage of reducing the required outdoor air supplied to the house during the defrost operation by air recirculation, which might affect the indoor air quality. A dual-core ERV also reduces the heating energy use, but in addition supplies a continuous outdoor air flow rate in compliance with standards. This paper evaluates the advantage of using the dual-core ERV in northern houses. First, this paper presents new correlation-based models of supply air temperature and humidity after the ERV unit, based on laboratory-controlled experimental data. Second, the energy use for ventilation and heating, and ventilation rates are simulated with TRNSYS program for northern houses at three arctic locations. They are compared with Montreal conditions as reference. The single-core ERV unit significantly reduces heating energy use in arctic locations by about 27%, compared with the case without heat recovery, however the outdoor airflow rate during the defrost is smaller than minimum standard requirements by about 13% in Kuujjuaq (below Arctic Circle) and 24% in Resolute (above Arctic Circle). The dual-core ERV unit removes the frost while continuously supplying the minimum required outdoor air to the indoors, however at the cost of minor increase of the heating and fan energy use compared with the single-core ERV unit.

Scale modeling study of airflow distribution uniformity in large spaces with high heat flux

• The uniformity of a large-scale sphere-shaped facility was experimentally explored. • Proposed reference coefficient to assess temperature uniformity in occupied zone. • Optimal ventilation design relied on airflow characteristics and practicality. With regard to the arrangement of air conditioning and ventilation in a building with large spaces, the uniformity of airflow distribution is often related to the air supply mode, heat flux, and location of the heat source. In this study, a 1:38 small-scale model of a large-scale sphere-shaped facility with high heat flux dissipation was constructed for investigating and analyzing the airflow distribution in the upper occupied zone. Additionally, a series of environmental parameters was experimentally obtained to characterize the influence of the location and release rate of the heat source. A dimensionless method was used to process the acquired data and explore the airflow characteristics. The experimental results revealed the evident influence of the release rate and heat source positions on the temperature distribution in the upper occupied zone. Furthermore, a reference temperature non-uniformity coefficient was proposed to assess the temperature uniformity in the occupied zone. Ultimately, the coefficients of temperature non-uniformity, reference temperature non-uniformity, and energy efficiency were reliably predicted. Consequently, a supply air temperature of 18 °C was selected as the optimal design parameter after conducting experiments to maintain the upper occupied zone temperature at 21 ± 1 °C and the corresponding minimum airflow rate could significantly reduce the energy consumption of air processing.

Regression Models for Performance Prediction of Internally-Cooled Liquid Desiccant Dehumidifiers

In this study, using response surface methodology and central composite design, regression models were developed relating 12 input factors to the supply air outlet humidity ratio and temperature of 4-fluid internally-cooled liquid desiccant dehumidifiers. The selected factors are supply air inlet temperature, supply air inlet humidity ratio, exhaust air inlet temperature, exhaust air inlet humidity ratio, liquid desiccant inlet temperature, liquid desiccant concentration, liquid desiccant flow rate, supply air mass flow rate, the ratio of exhaust to supply air mass flow rate, the thickness of the channel, the channel length, and the channel width of the dehumidifier. The designed experiments were performed using a numerical two-dimensional heat and mass transfer model of the liquid desiccant dehumidifier. The numerical model predicted the measured values of the supply air outlet humidity ratio within 6.7%. The regression model’s predictions of the supply air outlet humidity ratio matched the numerical model’s predictions and measured values within 4.5% and 7.9%, respectively. The results showed that the input factors with the most significant effect on the dehumidifying process in order of significance from high to low are as follows: supply air inlet humidity ratio, liquid desiccant concertation, length of channels, and width of channels. The developed regression models provide a straightforward means for performance prediction and optimization of internally-cooled liquid desiccant dehumidifiers.

A Numerical and Experimental Investigation of a Confluent Jets Ventilation Supply Device in a Conference Room

In this study, confluent jets ventilation (CJV) supply devices with three different nozzle arrays (1 × 19, 2 × 19, 3 × 19) were investigated both numerically and experimentally at two different airflow and supply air temperature set-ups. The performance of the CJV supply devices was investigated concerning thermal comfort, indoor air quality (IAQ), and heat removal effectiveness in a conference room environment. A comparison between the experimental and numerical results showed that the ϑ2¯−f model had the best agreement out of the investigated turbulence models. The numerical results showed that the size of the array had a great impact both on near-field development and on the conditions in the occupied zone. A larger array with multiple rows and a lower momentum conserved the inlet temperature and the mean age of the air better than a single-row array with a higher momentum. A larger array with multiple rows had a higher IAQ and a greater heat removal effectiveness in the occupied zone because the larger array conserved the mean age of air better and the buoyancy driven flow was slightly better at removing the heat. Because of the lower inlet velocities, they also had lower velocities at ankle level, which decreased the risk of draft and thermal discomfort.

Machine learning model of regenerative evaporative cooler for performance prediction based on experimental investigation

The regenerative evaporative cooler is experimentally investigated in this study and the predictive machine learning model is developed based on the test data to predict the energy and exergy performances of the device. This statistical machine learning framework is used to study the effect of six input operating variables (air inlet temperature, inlet air flow rate, air inlet specific humidity, water flow rate, inlet water temperature and extraction ratio) on the output variables (supply air temperature, cooling capacity, dew point effectiveness, coefficient of performance and exergy efficiency). The linear and polynomial regression models are followed to see the exactness of the model and check the train score against the test score. The effect of all the input variables on the output variables are discussed as well. In the case study, the developed machine learning model is used to predict the performances of fabricated regenerative evaporative cooler based on the field weather data of different days of 2020 in Varanasi. Model train score is 0.9974; the test score is 0.9912, the mean squared error is 0.1427, and the root mean squared error is 0.3778. This model can be used to predict the cooler performance under varied operating conditions.

Performance test of medium-temperature chiller with induction air-conditioning system

Abstract In this paper, medium-temperature chiller with induction air-conditioning system is introduced. Several onsite measurements were carried out on temperature and relative humidity distribution and energy consumption in summer 2020 in order to study the potential performance when applied in hot summer cold winter zone. The performance test results show that (1) the indoor temperature and humidity both comply with the range of design parameters; (2) the corresponding indoor air dew point temperature is much lower than the supply air temperature of air inductive unit, which implies no condensed water or dew on surface of air inductive unit; (3) the performance of the circulation pump, extensive type air handling unit fan; and medium-temperature chiller has exceeded the requirements of national standards.

Adaptive control algorithm with a retraining technique to predict the optimal amount of chilled water in a data center cooling system

We developed control algorithms based on one of three artificial-intelligence-based retraining techniques (sliding window, vector adaptation, and vector augmentation) to provide the optimal indoor temperature and save on the energy expenditure for cooling in data centers. The artificial neural network prediction model predicts the computer room air handler supply air temperature of a central chilled water system and is added to the control algorithm. The proposed algorithm can determine the optimal chilled water flow rate required to cool the server to the set temperature by using the predicted computer room air handler supply air temperature. We developed a control algorithm embedded in an artificial neural network predictive model that includes three retraining techniques. Afterward, we compared the control performance and verified its adaptability by using computer simulation. When using the algorithm with sliding window control, the root mean-squared error between the set temperature and the control temperature was 0.08 °C, the maximum error was 0.81 °C, and the cooling load was 21,026.27 kWh. The accuracy, stability, and energy-saving ability of the sliding window control algorithm were higher those of the other two algorithms, and its superior adaptability and scalability under changing environmental conditions were demonstrated.

Experimental investigation of an integrated absorption- solid desiccant air conditioning system

Separate handling of sensible and latent components of building loads can be an energy efficient approach compared to simultaneous management of both. This paper presents a detailed experimental analysis of an energy efficient air conditioning system using solid desiccant integrated with absorption system for separate building thermal load handling. The experimental system consists of silica-gel based solid desiccant for latent load handling, whereas the gas fired air-cooled NH3-H2O absorption chiller is used to handle sensible load of the space. A chilled water-cooling coil heat exchanger is installed on the process side of desiccant cooling system to integrate it with the absorption chiller. The performance of integrated absorption- solid desiccant system is compared with a standalone conventional desiccant air conditioning system with a direct evaporative cooler considering it as baseline system under wide range of operating conditions including air temperature, air humidity, and regeneration temperature. The comparative analysis is performed in terms of supply air temperature, cooling capacity, and coefficient of performance. The experimental results exhibit that supply air temperature of the integrated system is 15.2 °C compared to 24.6 °C achieved by conventional desiccant system. Moreover, COPth of the integrated system is also 50–55% higher than its counterpart and it is almost comparable with double-effect absorption chiller. It is concluded that the integrated system using separate load management approach is more efficient for HVAC applications.

Effect of compression ignition engine preheating on its performance under cold start conditions

This paper examines the effect of an external preheating system for an internal combustion engine on fuel consumption, CO2 emissions, and cabin temperature of a Euro4 vehicle. A 1 kW electric system powered by 220 V was installed in series in the cooling system of a vehicle with a compression-ignition engine of 2.5 dm3 capacity. The tests were carried out in simulated urban driving conditions (distance of 4.2 km), extra-urban driving conditions (distance of 17 km), and during idling at cold-start temperatures ranging from -10 oC to 2 oC. Preheating the engine under simulated city conditions reduces fuel consumption by 2.64 dm3/100 km and increases the supply air temperature immediately after engine start-up. Due to the preheater being powered from an external power grid, the cost per trip and total CO2 emissions are increased. Assuming renewable energy sources, CO2 emissions would be reduced the most for the stationary tests after engine preheating. In contrast, emissions would be reduced the least for extra-urban driving.

In-duct phase change material-based energy storage to enhance building demand flexibility

This paper presents a novel energy storage solution by incorporating phase change material (PCM) panels in supply ducts to increase a building’s thermal storage capacity and demand flexibility. During off-peak hours, the system runs at a supply air temperature (SAT) below the PCM solidification point to charge the storage unit with “cooling” energy. During on-peak hours, a higher SAT is utilized so that the stored “cooling” energy can be discharged into the supply-air as a means to reduce the peak air-conditioning power usage. To evaluate the potentials of peak demand reduction and utility cost savings, a numerical model for a PCM panel prototype and its heat exchange with the air flow in the duct was developed and calibrated using experimental data. Whole building energy simulations were conducted in a co-simulation environment that has integrated the developed PCM model, EnergyPlus Department of Energy (DOE) prototypical model for a medium office building and a calibrated model for variable-speed direct-expansion cooling systems. The simulations covered five cities in different U.S. climate zones over a three-month cooling season and used actual time-of-use (TOU) rate schedules offered by the local electric utility companies. The simulation results have shown the PCM storage could reduce the on-peak energy consumption by 23–32% and the seasonal cooling electricity cost by up to 16%, with a simple rule-based control strategy. A simple payback analysis resulted in payback periods from 7.5 to 27 cooling months.

Investigating the flexibility of a novel multi-zone air heating and ventilation system using model predictive control

In forced air heating systems, there are basically two approaches to set the temperature of each zone within a multi-zone building: regulating the supply airflow rate and regulating the supply air temperature (or a combination of these two parameters). Conventional variable air volume (VAV) systems usually maintain the supply air temperature constant within all rooms and regulate only the supply airflow rate to the room. Recently, a novel multi-zone air heating and ventilation (MZHV) system has been developed, which is capable of regulating both the supply airflow rate and the supply air temperature to the rooms independently of each other. This paper investigates the flexibility of the novel MZHV system and compares it with the conventional VAV systems. The MZHV system as well as a multi-zone case study building are modeled in a high-fidelity simulation environment (IDA ICE). Instead of traditional rule-based control schemes, a model predictive control (MPC) scheme is developed to control real-time power consumption of the multi-zone building. For this purpose, a second-order state space model is identified for each zone and calibrated against the results from the high fidelity simulations. MPC is implemented in MATLAB environment by using the second-order state space models. Two co-simulations are performed to compare the flexibility of the novel MZHV system with a conventional VAV system. The results show that the MZHV system provides a significant flexibility in terms of the energy usage, without affecting the comfort levels. This flexibility can for example be exploited to reduce the overall energy usage or provide ancillary services for a smart electricity grid.